Overview

Dataset statistics

Number of variables13
Number of observations1380
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory140.3 KiB
Average record size in memory104.1 B

Variable types

Numeric12
DateTime1

Alerts

unpaid has constant value "0" Constant
df_index is highly correlated with relative_hourHigh correlation
gpu_memory_usage is highly correlated with gpu_load and 2 other fieldsHigh correlation
gpu_load is highly correlated with gpu_memory_usage and 2 other fieldsHigh correlation
gpu_temp is highly correlated with gpu_memory_usage and 2 other fieldsHigh correlation
reported_hashrate is highly correlated with gpu_memory_usage and 2 other fieldsHigh correlation
relative_hour is highly correlated with df_indexHigh correlation
cpu_load is highly skewed (γ1 = 31.45408782) Skewed
df_index has unique values Unique
ts has unique values Unique
relative_hour has unique values Unique
gpu_load has 282 (20.4%) zeros Zeros
unpaid has 1380 (100.0%) zeros Zeros
reported_hashrate has 282 (20.4%) zeros Zeros

Reproduction

Analysis started2021-12-01 03:31:26.272914
Analysis finished2021-12-01 03:31:46.392196
Duration20.12 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct1380
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean820.3782609
Minimum0
Maximum1553
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2021-11-30T22:31:46.460497image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile68.95
Q1518.75
median863.5
Q31208.25
95-th percentile1484.05
Maximum1553
Range1553
Interquartile range (IQR)689.5

Descriptive statistics

Standard deviation457.4446574
Coefficient of variation (CV)0.55760212
Kurtosis-1.137130293
Mean820.3782609
Median Absolute Deviation (MAD)345
Skewness-0.2299526691
Sum1132122
Variance209255.6146
MonotonicityStrictly increasing
2021-11-30T22:31:46.706033image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
10911
 
0.1%
10991
 
0.1%
10981
 
0.1%
10971
 
0.1%
10961
 
0.1%
10951
 
0.1%
10941
 
0.1%
10931
 
0.1%
10921
 
0.1%
Other values (1370)1370
99.3%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
ValueCountFrequency (%)
15531
0.1%
15521
0.1%
15511
0.1%
15501
0.1%
15491
0.1%

ts
Date

UNIQUE

Distinct1380
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
Minimum2021-11-11 08:40:46-05:00
Maximum2021-11-11 13:10:12-05:00
2021-11-30T22:31:46.956831image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:47.092158image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

cpu_load
Real number (ℝ≥0)

SKEWED

Distinct11
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2588405797
Minimum0.1
Maximum12.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2021-11-30T22:31:47.252348image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.2
median0.2
Q30.3
95-th percentile0.4
Maximum12.5
Range12.4
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.3488114544
Coefficient of variation (CV)1.347591845
Kurtosis1101.82932
Mean0.2588405797
Median Absolute Deviation (MAD)0.1
Skewness31.45408782
Sum357.2
Variance0.1216694307
MonotonicityNot monotonic
2021-11-30T22:31:47.354367image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.2655
47.5%
0.3442
32.0%
0.1155
 
11.2%
0.460
 
4.3%
0.524
 
1.7%
0.620
 
1.4%
0.714
 
1.0%
0.84
 
0.3%
0.94
 
0.3%
12.51
 
0.1%
ValueCountFrequency (%)
0.1155
 
11.2%
0.2655
47.5%
0.3442
32.0%
0.460
 
4.3%
0.524
 
1.7%
ValueCountFrequency (%)
12.51
 
0.1%
11
 
0.1%
0.94
 
0.3%
0.84
 
0.3%
0.714
1.0%

cpu_freq
Real number (ℝ≥0)

Distinct1335
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean940.7778841
Minimum802.77
Maximum3593.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2021-11-30T22:31:47.501455image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum802.77
5-th percentile818.825
Q1845.2625
median878.865
Q3930.65
95-th percentile1170.6605
Maximum3593.97
Range2791.2
Interquartile range (IQR)85.3875

Descriptive statistics

Standard deviation280.275704
Coefficient of variation (CV)0.2979191037
Kurtosis52.56019129
Mean940.7778841
Median Absolute Deviation (MAD)40.43
Skewness6.736760048
Sum1298273.48
Variance78554.47024
MonotonicityNot monotonic
2021-11-30T22:31:47.751619image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1002.152
 
0.1%
979.572
 
0.1%
816.342
 
0.1%
1097.942
 
0.1%
817.082
 
0.1%
998.392
 
0.1%
1097.832
 
0.1%
883.572
 
0.1%
879.142
 
0.1%
942.782
 
0.1%
Other values (1325)1360
98.6%
ValueCountFrequency (%)
802.771
0.1%
806.091
0.1%
806.141
0.1%
807.91
0.1%
808.221
0.1%
ValueCountFrequency (%)
3593.971
0.1%
3593.781
0.1%
3593.631
0.1%
3593.561
0.1%
3593.191
0.1%

memory_usage
Real number (ℝ≥0)

Distinct1228
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1832794821
Minimum1358897152
Maximum1992646656
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2021-11-30T22:31:47.917285image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1358897152
5-th percentile1361993318
Q11892032512
median1979025408
Q31985352704
95-th percentile1988375142
Maximum1992646656
Range633749504
Interquartile range (IQR)93320192

Descriptive statistics

Standard deviation247732712.2
Coefficient of variation (CV)0.1351666369
Kurtosis-0.5470334329
Mean1832794821
Median Absolute Deviation (MAD)7792640
Skewness-1.17238198
Sum2.529256854 × 1012
Variance6.137149669 × 1016
MonotonicityNot monotonic
2021-11-30T22:31:48.072001image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19866542083
 
0.2%
18937856003
 
0.2%
19868835843
 
0.2%
13611868163
 
0.2%
19858882563
 
0.2%
19768360963
 
0.2%
19793633283
 
0.2%
19857326082
 
0.1%
19862896642
 
0.1%
19857571842
 
0.1%
Other values (1218)1353
98.0%
ValueCountFrequency (%)
13588971521
0.1%
13594869761
0.1%
13597040641
0.1%
13597450241
0.1%
13599293441
0.1%
ValueCountFrequency (%)
19926466561
0.1%
19908116481
0.1%
19906273281
0.1%
19905945601
0.1%
19904348161
0.1%

cpu_temp
Real number (ℝ≥0)

Distinct11
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.28985507
Minimum25
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2021-11-30T22:31:48.199250image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile26
Q131
median32
Q333
95-th percentile33
Maximum35
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.247237926
Coefficient of variation (CV)0.07182001709
Kurtosis0.3636149802
Mean31.28985507
Median Absolute Deviation (MAD)1
Skewness-1.252130337
Sum43180
Variance5.050078297
MonotonicityNot monotonic
2021-11-30T22:31:48.295428image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
32467
33.8%
33412
29.9%
31117
 
8.5%
2797
 
7.0%
2689
 
6.4%
3464
 
4.6%
2951
 
3.7%
3050
 
3.6%
2830
 
2.2%
252
 
0.1%
ValueCountFrequency (%)
252
 
0.1%
2689
6.4%
2797
7.0%
2830
 
2.2%
2951
3.7%
ValueCountFrequency (%)
351
 
0.1%
3464
 
4.6%
33412
29.9%
32467
33.8%
31117
 
8.5%

gpu_memory_usage
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1829049353
Minimum9437184
Maximum2296381440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2021-11-30T22:31:48.374067image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum9437184
5-th percentile9437184
Q12296381440
median2296381440
Q32296381440
95-th percentile2296381440
Maximum2296381440
Range2286944256
Interquartile range (IQR)0

Descriptive statistics

Standard deviation922485666.4
Coefficient of variation (CV)0.5043525288
Kurtosis0.1553546084
Mean1829049353
Median Absolute Deviation (MAD)0
Skewness-1.468036634
Sum2.524088107 × 1012
Variance8.509798046 × 1017
MonotonicityNot monotonic
2021-11-30T22:31:48.453852image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
22963814401098
79.6%
9437184282
 
20.4%
ValueCountFrequency (%)
9437184282
 
20.4%
22963814401098
79.6%
ValueCountFrequency (%)
22963814401098
79.6%
9437184282
 
20.4%

gpu_load
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.56521739
Minimum0
Maximum100
Zeros282
Zeros (%)20.4%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2021-11-30T22:31:48.535456image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.33704206
Coefficient of variation (CV)0.5069682881
Kurtosis0.1553546084
Mean79.56521739
Median Absolute Deviation (MAD)0
Skewness-1.468036634
Sum109800
Variance1627.076962
MonotonicityNot monotonic
2021-11-30T22:31:48.640585image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1001098
79.6%
0282
 
20.4%
ValueCountFrequency (%)
0282
 
20.4%
1001098
79.6%
ValueCountFrequency (%)
1001098
79.6%
0282
 
20.4%

gpu_temp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct50
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.46956522
Minimum29
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2021-11-30T22:31:48.755912image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile29
Q162
median64
Q366
95-th percentile71
Maximum78
Range49
Interquartile range (IQR)4

Descriptive statistics

Standard deviation13.92650661
Coefficient of variation (CV)0.2381838579
Kurtosis0.1678195859
Mean58.46956522
Median Absolute Deviation (MAD)2
Skewness-1.356504881
Sum80688
Variance193.9475865
MonotonicityNot monotonic
2021-11-30T22:31:48.896127image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64245
17.8%
63196
14.2%
65159
11.5%
66112
 
8.1%
29108
 
7.8%
6794
 
6.8%
6860
 
4.3%
3047
 
3.4%
6940
 
2.9%
7137
 
2.7%
Other values (40)282
20.4%
ValueCountFrequency (%)
29108
7.8%
3047
3.4%
3116
 
1.2%
3235
 
2.5%
3318
 
1.3%
ValueCountFrequency (%)
781
 
0.1%
772
 
0.1%
761
 
0.1%
757
0.5%
7410
0.7%

hashrate
Real number (ℝ≥0)

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2502415.456
Minimum1111111.11
Maximum4444444.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2021-11-30T22:31:49.002937image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1111111.11
5-th percentile1111111.11
Q12222222.22
median2222222.22
Q33333333.33
95-th percentile3333333.33
Maximum4444444.44
Range3333333.33
Interquartile range (IQR)1111111.11

Descriptive statistics

Standard deviation800766.1333
Coefficient of variation (CV)0.3199972775
Kurtosis-0.03710761797
Mean2502415.456
Median Absolute Deviation (MAD)0
Skewness0.2794040705
Sum3453333330
Variance6.412264003 × 1011
MonotonicityNot monotonic
2021-11-30T22:31:49.093774image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
2222222.22756
54.8%
3333333.33396
28.7%
1111111.11168
 
12.2%
4444444.4460
 
4.3%
ValueCountFrequency (%)
1111111.11168
 
12.2%
2222222.22756
54.8%
3333333.33396
28.7%
4444444.4460
 
4.3%
ValueCountFrequency (%)
4444444.4460
 
4.3%
3333333.33396
28.7%
2222222.22756
54.8%
1111111.11168
 
12.2%

unpaid
Real number (ℝ≥0)

CONSTANT
REJECTED
ZEROS

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros1380
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2021-11-30T22:31:49.186931image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2021-11-30T22:31:49.262881image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
01380
100.0%
ValueCountFrequency (%)
01380
100.0%
ValueCountFrequency (%)
01380
100.0%

reported_hashrate
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2546086.957
Minimum0
Maximum3200000
Zeros282
Zeros (%)20.4%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2021-11-30T22:31:49.341406image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13200000
median3200000
Q33200000
95-th percentile3200000
Maximum3200000
Range3200000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1290785.346
Coefficient of variation (CV)0.5069682881
Kurtosis0.1553546084
Mean2546086.957
Median Absolute Deviation (MAD)0
Skewness-1.468036634
Sum3513600000
Variance1.666126809 × 1012
MonotonicityNot monotonic
2021-11-30T22:31:49.419165image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
32000001098
79.6%
0282
 
20.4%
ValueCountFrequency (%)
0282
 
20.4%
32000001098
79.6%
ValueCountFrequency (%)
32000001098
79.6%
0282
 
20.4%

relative_hour
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct1380
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.374058575
Minimum0
Maximum4.490555556
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2021-11-30T22:31:49.548959image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1995833333
Q11.502083333
median2.499305556
Q33.49625
95-th percentile4.292361111
Maximum4.490555556
Range4.490555556
Interquartile range (IQR)1.994166667

Descriptive statistics

Standard deviation1.323159755
Coefficient of variation (CV)0.557340821
Kurtosis-1.137122061
Mean2.374058575
Median Absolute Deviation (MAD)0.9977777778
Skewness-0.2315092025
Sum3276.200833
Variance1.750751738
MonotonicityStrictly increasing
2021-11-30T22:31:49.753383image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
3.15751
 
0.1%
3.1808333331
 
0.1%
3.1777777781
 
0.1%
3.1751
 
0.1%
3.1719444441
 
0.1%
3.1691666671
 
0.1%
3.1661111111
 
0.1%
3.1633333331
 
0.1%
3.1605555561
 
0.1%
Other values (1370)1370
99.3%
ValueCountFrequency (%)
01
0.1%
0.0030555555561
0.1%
0.0058333333331
0.1%
0.0088888888891
0.1%
0.011666666671
0.1%
ValueCountFrequency (%)
4.4905555561
0.1%
4.48751
0.1%
4.4847222221
0.1%
4.4819444441
0.1%
4.4788888891
0.1%

Interactions

2021-11-30T22:31:43.823157image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:27.809301image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:29.316015image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:30.627964image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:32.008769image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:33.416519image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:34.793753image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:36.163383image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:37.606164image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:39.079311image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:40.636501image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:42.276098image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:43.983021image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:27.980289image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:29.430650image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:30.743002image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:32.126297image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:31:33.527461image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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Correlations

2021-11-30T22:31:49.874244image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-30T22:31:50.053661image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-30T22:31:50.229741image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-30T22:31:50.404290image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-30T22:31:45.956839image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-30T22:31:46.308609image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indextscpu_loadcpu_freqmemory_usagecpu_tempgpu_memory_usagegpu_loadgpu_temphashrateunpaidreported_hashraterelative_hour
002021-11-11 08:40:46-05:0012.5875.35136107212828.09437184.00.033.02222222.2200.00.000000
112021-11-11 08:40:57-05:000.21126.39136207155227.09437184.00.033.02222222.2200.00.003056
222021-11-11 08:41:07-05:000.2813.76136222310427.09437184.00.033.02222222.2200.00.005833
332021-11-11 08:41:18-05:000.1844.80136284569628.09437184.00.033.02222222.2200.00.008889
442021-11-11 08:41:28-05:000.2892.35136198553627.09437184.00.033.02222222.2200.00.011667
552021-11-11 08:41:38-05:000.1816.02136110899228.09437184.00.032.02222222.2200.00.014444
662021-11-11 08:41:49-05:000.3996.34136097792028.09437184.00.032.02222222.2200.00.017500
772021-11-11 08:41:59-05:000.1834.96136000307228.09437184.00.032.02222222.2200.00.020278
882021-11-11 08:42:10-05:000.23593.97136055603227.09437184.00.032.02222222.2200.00.023333
992021-11-11 08:42:20-05:000.1849.03136093696027.09437184.00.032.02222222.2200.00.026111

Last rows

df_indextscpu_loadcpu_freqmemory_usagecpu_tempgpu_memory_usagegpu_loadgpu_temphashrateunpaidreported_hashraterelative_hour
137015442021-11-11 13:08:39-05:000.1820.21189497344028.09437184.00.032.01111111.1100.04.464722
137115452021-11-11 13:08:49-05:000.11045.11189410099230.09437184.00.032.01111111.1100.04.467500
137215462021-11-11 13:08:59-05:000.1958.21189357260829.09437184.00.032.01111111.1100.04.470278
137315472021-11-11 13:09:10-05:000.2835.80189407232028.09437184.00.032.01111111.1100.04.473333
137415482021-11-11 13:09:20-05:000.1878.80189404774429.09437184.00.032.01111111.1100.04.476111
137515492021-11-11 13:09:30-05:000.11170.61189405593628.09437184.00.032.01111111.1100.04.478889
137615502021-11-11 13:09:41-05:000.1874.17189378969629.09437184.00.032.01111111.1100.04.481944
137715512021-11-11 13:09:51-05:000.1831.05189407232029.09437184.00.032.01111111.1100.04.484722
137815522021-11-11 13:10:01-05:000.1885.64189378969629.09437184.00.032.01111111.1100.04.487500
137915532021-11-11 13:10:12-05:000.1835.07189430579229.09437184.00.032.01111111.1100.04.490556